Personalized pain medicine: focusing on modalities in a patient’s everyday life and the importance of assessing how to manage their migraines

Each drug needs at least six weeks to see if it works, before trying the next one. Each drug can cause side effects, whether or not it eases the pain. After cycling through each migraine drug individually, the team started testing drug combinations. “We finally got control of her migraines on the 24th medication trial,” Russo says.

Personalized pain medicine doesn’t need to be perfect for it to offer a major improvement over today’s chronic-pain treatment. The doctors are doing a mental coin toss between treatments. He says if an individual prediction accuracy of 70% is reached, it could help the person feel better about their quality of life.

It is impractical to run a battery of tests outside of the lab. Lin has, therefore, been working to streamline the system and concentrating on the key modalities that most accurately reflect pain levels. She wants to identify three or four crucial indicators. This refinement is necessary, she says, to ensure the tool’s practicality in health-care settings.

An optimistic person who breaks their arm mountain biking on a weekend away with friends to celebrate a promotion could experience pain differently to a pessimistic person who breaks an arm cycling from their monotonous job to their home, on which they are behind on the mortgage repayments. Anxiety and depression can be risk factors for the onset of chronic pain. “Nobody experiencing chronic pain is in the same place at the same time,” she says. Everything else in their life is intertwined with the pain, from what they have experienced to what they are anticipating.

Sensitivity to Stimuli: A Core-Collaborative Study of Pain Using Machine Learning, EEG and Brain Scans

Washed out to the human eye, at least. “When we compared the EEGs of people with pain versus those with no pain, standard statistical analysis showed no difference,” Saab says. When Saab and his collaborators trialled a basic form of machine learning to process the data, however, the artificial intelligence (AI) algorithm could detect something. The algorithm could distinguish between people with chronic lower back pain who would benefit from a spine cord stimulator and those that wouldn’t, as well as between healthy controls and those who wouldn’t. The algorithm had an of accuracy almost 80%, Saab says, showing that even a simple AI can outperform conventional statistics for the task. “Basic machine learning is picking up signals that classic methods and the human eye are not capable of detecting.”

Vollert and his team used machine learning to analyse people’s sensitivity to stimuli such as pinpricks, heat and touch, and they were able to group them into three distinct subtypes that were linked to a hypothesis about neuropathic pain mechanisms.

The results were promising. In 2012 we were able to accurately classify the presence or absence of lower back pain using structural brain scans. Progress then stalled. He says that the accuracy hasn’t improved since then.

A stronger signal should emerge from pooling data from multiple techniques. “Where the field is going is to bring all these data sets together,” says Vollert, who is currently participating in two such efforts in Europe: the IMI-PainCare Biopain consortium led by Rolf-Detlef Treede, a neuroscientist at the University of Heidelberg in Mannheim, Germany, and Painstorm, led by David Bennett, a neurologist at the University of Oxford, UK. Vollert says that prospective data sets collect all kinds of marker information in parallel from the same patients. That is the kind of data set we are generating at the moment.

With multimodal data in hand, advanced AI tools that are more powerful than the simple machine-learning models previously deployed will be crucial to categorizing people into subgroups and treatment selection, says Vollert. He says they’re working with machine-learning specialists to find models that go deeper into the data.

Preliminary findings suggest that advanced deep-learning AI could be transformative for accurate pain biomarker identification, says Mackey. “It’s not published yet, but we’ve just been using some really advanced AI techniques on UK Biobank structural imaging data from a highly heterogeneous population with back pain, and we’re getting classification accuracy around 90%.” The next step — which efforts such as the expanded CHOIR platform are intended to enable — is to link treatment outcomes to this new-found capability to accurately and objectively classify people with pain into subgroups.

Even given this opacity, physicians could still consider non-interpretable models’ output as one factor in their decision-making process for treatments, Mackey says. In the meantime, the next generation of AI models that are currently in development might show their work more readily. “AI research is moving towards models that are interpretable, but which approach the accuracies of these deep learning, non-interpretable models,” he says.

The problem for pain-physicians is non-interpretable modelling. “As clinicians, we want to understand the basis of a medical recommendation and not just blindly take the word of a machine — because what if the machine is wrong?”

After coming under Russo’s care, it took three long years to find a way to control her migraines. There were plenty of options for people with chronic migraines. Finding a cure for chronic pain is not easy, due to the fact that treatments are always being tested one by one in the hope of finding a match.

It was a daily occurrence of her ferocious migraines. She vomited when she had to lie down between patient consults at the Hunter Pain Specialists in Broadmeadow, Australia. “Then she’d have to get up, rinse her mouth and see the next patient.”

Low Back Pain Analysis Using an Artificial Intelligence-enabled Wearable Device: COMPASS for chronic pain management, a case study

Lin acknowledges that a lot of work remains, even though it is not really working. Maybe we should stop. As I withdraw my hand from the icy cold, the pain quickly subsides — and I don’t need an objective measure to tell me when, after a few moments, the pain is gone.

The platform malfunctioned. With my hand submerged in the ice water, the pain scores on the app generally trended upwards. The readings fluctuated, however, as I experienced a feeling of pain.

When I tried the cold-exposure task in her lab, I had only one sensor on my body feeding data into COMPASS. Monitoring of brain activity, eye diameter, and facial tracking were not recorded.

The researchers found that an inference derived from nine measures was more efficient than any other approach when compared with the self-reported pain scores. Front. There was a Neurosci. 16 that was published in the year 2022.

Lin is using the data from people with Chronic Low Back Pain to improve her platform. A variety of data sets has proven the ability of these artificial-intelligence tools to provide insights that align closely with people’s own pain assessments.

At a point in time, the technology provides a snapshot of a person’s pain. That is fine for an assessment by a doctor, but it can’t show the changes in pain levels over time.

Costs and complexity make these technologies unsuitable for clinical use. Diagnostic tools and Wearable Devices come in when it’s practical and portable.

The platform is authorized in the United States only for use during surgery with people who are under anaesthesia and unable to communicate their pain levels. By allowing clinicians to adjust painkiller dosages in line with an individual’s pain, it aims to optimize pain management and mitigate the risks associated with unnecessary opioid prescriptions.

Moreover, many individuals — including babies, people who are non-verbal, people who are critically injured and those under sedation — cannot communicate their symptoms effectively. This makes it more difficult to manage and treat pain and impedes the delivery of analgesia.

As discomfort escalates to pain, a sensor strapped to my chest detects changes in my heart rate, breathing pattern, skin conductance and other bodily responses. The signals are processed into a pain score. Displayed on a smartphone app, my pain level is 4.

The graduate student bears down on my arm with a force akin to a firm handshake. The sensation starts to hurt when you concentrate on a patch of skin in the same size as a small coin.